## Loading required package: pacman

FIRST STEPS

Dataset manipulation in ‘bl_schisto_long_25052024_BW’.

Exclusion of HIV-HBV co-infected patients

## [1] "HBV"
## [1] 799

Create ‘CCA_Fb’ dataset with HBV-monoinfected patients who have been tested for CCA (n = 721)

## No variable names specified - using all columns.
## No duplicate combinations found of: pid, cca_res, cca_bl_date, fibs_med_res, fibs_iqr_res, cap_med_res, cap_iqr_res
## # A tibble: 0 × 8
## # ℹ 8 variables: pid <chr>, cca_res <fct>, cca_bl_date <date>,
## #   fibs_med_res <dbl>, fibs_iqr_res <dbl>, cap_med_res <dbl>,
## #   cap_iqr_res <dbl>, dupe_count <int>

Create ‘SENB_noCCA’ dataset with patients (both mono- & co-infected) who have never been tested for CCA (n = 127)

## No variable names specified - using all columns.
## No duplicate combinations found of: pid, enrol_d, cca_res, cca_bl_date, fibs_med, fibs_iqr, cap_med, cap_iqr
## # A tibble: 0 × 9
## # ℹ 9 variables: pid <chr>, enrol_d <date>, cca_res <fct>, cca_bl_date <date>,
## #   fibs_med <dbl>, fibs_iqr <dbl>, cap_med <dbl>, cap_iqr <dbl>,
## #   dupe_count <int>

List of SENB patients to be tested for CCA

## SENB1082, SENB1205, SENB1212, SENB1225, SENB1227, SENB1241, SENB1275, SENB1280, SENB1281, SENB1492, SENB1496, SENB1512, SENB1536, SENB1541, SENB1555, SENB1608, SENB1632, SENB1705, SENB2001, SENB2003, SENB2006, SENB2008, SENB2009, SENB2010, SENB2011, SENB2014, SENB2015, SENB2016, SENB2017, SENB2018, SENB2019, SENB2021, SENB2022, SENB2024, SENB2025, SENB2026, SENB2028, SENB2029, SENB2030, SENB2031, SENB2032, SENB2033, SENB2034, SENB2035, SENB2038, SENB2040, SENB2041, SENB2042, SENB2043, SENB2044, SENB2045, SENB2049, SENB2050, SENB2051, SENB2053, SENB2054, SENB2056, SENB2092, SENB2106, SENB2107, SENB2108, SENB2109, SENB2111, SENB2112, SENB2116, SENB2121, SENB2124, SENB2146, SENB2148, SENB2149, SENB2150, SENB2151, SENB2152, SENB2153, SENB2154, SENB2155, SENB2156, SENB2157, SENB2158, SENB2159, SENB2160, SENB2161, SENB2162, SENB2163, SENB2164, SENB2165, SENB2166, SENB2167, SENB2168, SENB2174, SENB2175, SENB2176, SENB2177, SENB2178, SENB2179, SENB2180, SENB2181, SENB2182, SENB2183, SENB2184, SENB2185, SENB2186, SENB2187, SENB2188, SENB2189, SENB2190, SENB2191, SENB2192, SENB2193, SENB2194, SENB2195, SENB2196, SENB2197, SENB2198, SENB2199, SENB2200, SENB2201, SENB2202, SENB2203, SENB2204, SENB2205, SENB2206, SENB2207, SENB2208, SENB2209, SENB2217, SENB2218

We want a table with the pids but also the enrolment date for the FibroScan so we can find the Urine samples easily in the lab

## Joining with `by = join_by(pid, enrol_d, cca_res, fibs_med, fibs_iqr, cap_med,
## cap_iqr)`

Create ‘SENBmono_noCCA’ dataset with (mono-infected) patients who have never been tested for CCA (n = 78)

## No variable names specified - using all columns.
## No duplicate combinations found of: pid, cca_res, cca_bl_date, fibs_med, fibs_iqr, cap_med, cap_iqr
## # A tibble: 0 × 8
## # ℹ 8 variables: pid <chr>, cca_res <fct>, cca_bl_date <date>, fibs_med <dbl>,
## #   fibs_iqr <dbl>, cap_med <dbl>, cap_iqr <dbl>, dupe_count <int>

SOCIO-DEMOGRAPHICS

Create temporary dataset with all socio-demographic variables

Select for variables of interest only

Analysis

##  age_en   n percent
##   18-31 384  48.06%
##     >31 415  51.94%
##   Total 799 100.00%
##     sex   n percent
##    Male 427  53.44%
##  Female 372  46.56%
##   Total 799 100.00%
##               orig_endemicity   n percent valid_percent
##       1-9.9% prevalence (low) 183  22.90%        22.93%
##  10-49% prevalence (moderate) 245  30.66%        30.70%
##  <1% prevalence (non-endemic) 273  34.17%        34.21%
##                 Other Country  17   2.13%         2.13%
##        ≥50% prevalence (high)  80  10.01%        10.03%
##                          <NA>   1   0.13%             -
##                         Total 799 100.00%       100.00%
##  reg_act   n percent
##    Dakar 642  80.35%
##  Outside 157  19.65%
##    Total 799 100.00%

Creation of final sociodemographics table

## Warning in rm(list = "sociodemo1", "sociodemo2"): object 'sociodemo1' not found

COMORBIDITIES

Create temporary dataset with all comorbidities variables

## # A tibble: 22 × 2
##    diabete glu_res
##    <fct>     <dbl>
##  1 yes        1.4 
##  2 yes        1.96
##  3 yes        1.69
##  4 yes        0.7 
##  5 yes        0.75
##  6 yes        2.2 
##  7 yes        0.74
##  8 yes        2.43
##  9 yes        1.63
## 10 yes        0.86
## # ℹ 12 more rows

Analysis

##  alcohol_cons   n percent
##            no 744  93.12%
##       yes_now  34   4.26%
##      yes_past  21   2.63%
##         Total 799 100.00%
##  audit_cat   n percent
##     Misuse  15   1.88%
##   Negative 784  98.12%
##      Total 799 100.00%
##     drugs   n percent
##        no 786  98.37%
##  yes_now    0   0.00%
##  yes_past  13   1.63%
##     Total 799 100.00%
##  smoke_yn   n percent
##        no 710  88.86%
##   yes_now  18   2.25%
##  yes_past  71   8.89%
##     Total 799 100.00%
##    HTA   n percent
##     no 607  75.97%
##    yes 192  24.03%
##  Total 799 100.00%
##  diabete   n percent
##       no 777  97.25%
##      yes  22   2.75%
##    Total 799 100.00%
##        BMI_cat   n percent valid_percent
##  normal weight 459  57.45%        58.03%
##        obesity  59   7.38%         7.46%
##     overweight 156  19.52%        19.72%
##    underweight 117  14.64%        14.79%
##           <NA>   8   1.00%             -
##          Total 799 100.00%       100.00%
##  dyslipidemia   n percent
##            no 482  60.33%
##           yes 317  39.67%
##         Total 799 100.00%
##    COL   n percent
##     no 796  99.62%
##    yes   3   0.38%
##  Total 799 100.00%
##    AVC   n percent
##     no 799 100.00%
##    yes   0   0.00%
##  Total 799 100.00%
##  renal   n percent
##     no 799 100.00%
##    yes   0   0.00%
##  Total 799 100.00%
##  cancer   n percent
##      no 798  99.87%
##     yes   1   0.13%
##   Total 799 100.00%
##  liver_dis_FDR   n percent
##             no 784  98.12%
##            yes  15   1.88%
##          Total 799 100.00%
##  hcc_FDR   n percent
##       no 698  87.36%
##      yes 101  12.64%
##    Total 799 100.00%

Join sociodemographics & comorbidities datasets

I forgot to add the “dipstick EtG” variable…

## # A tibble: 799 × 224
##    pid      hiv_status enrol_d    center med   sen_yn country_orig region_orig
##    <chr>    <chr>      <date>     <fct>  <chr> <fct>  <fct>        <fct>      
##  1 SENB1002 HBV        2019-09-30 SMIT   1     yes    <NA>         Fatick     
##  2 SENB1003 HBV        2019-10-01 SMIT   1     yes    <NA>         Dakar      
##  3 SENB1004 HBV        2019-10-01 SMIT   1     yes    <NA>         Dakar      
##  4 SENB1005 HBV        2019-10-01 SMIT   1     yes    <NA>         Dakar      
##  5 SENB1006 HBV        2019-10-01 SMIT   1     yes    <NA>         Dakar      
##  6 SENB1007 HBV        2019-10-02 SMIT   1     yes    <NA>         Ziguinchor 
##  7 SENB1008 HBV        2019-10-02 SMIT   1     yes    <NA>         Kaolack    
##  8 SENB1009 HBV        2019-10-02 SMIT   1     yes    <NA>         Louga      
##  9 SENB1010 HBV        2019-10-02 SMIT   1     yes    <NA>         Diurbel    
## 10 SENB1011 HBV        2019-10-02 SMIT   1     no     Mali         <NA>       
## # ℹ 789 more rows
## # ℹ 216 more variables: dpt_orig <fct>, res_time <dbl>, reg_act <fct>,
## #   dpt_act <fct>, birth_d <date>, age_en <dbl>, sex <fct>, rank <dbl>,
## #   marital <fct>, union <fct>, child <dbl>, education <fct>, employ <fct>,
## #   other_employ <chr>, health_ins <fct>, hiv_yn <fct>, type_HIV <chr>,
## #   hiv_test1 <fct>, hiv_test1_date <date>, hiv_test2 <fct>,
## #   hiv_test2_date <date>, CD4_last <dbl>, CD4_last_date <date>, …

Now we got the full sociodemographics + comorbidities table

HBV MARKERS

Create temporary dataset with all ‘HBV markers’ variables

Analysis

##  screen_d   n percent
##      2019  82  10.26%
##      2020  59   7.38%
##      2021 409  51.19%
##      2022 243  30.41%
##      2023   6   0.75%
##     Total 799 100.00%
##     cvb_detection   n percent
##  2001-20000 IU/mL 144  18.02%
##     21-2000 IU/mL 435  54.44%
##         ≤20 IU/mL 150  18.77%
##      ≥20000 IU/mL  70   8.76%
##             Total 799 100.00%
##      qAgHBs_cat   n percent
##  100-1000 IU/mL 101  12.64%
##      <100 IU/mL  71   8.89%
##     >1000 IU/mL 627  78.47%
##           Total 799 100.00%
##  AgHBe_res   n percent
##   Positive  19   2.38%
##   Negative 780  97.62%
##      Total 799 100.00%

Join ‘hbv_markers’ with ‘sociodemo_como’

HCV/HDV MARKERS

Create temporary dataset with all ‘HCV/HDV markers’ variables

Analysis

##  AcVHC_res   n percent
##   Positive   2   0.25%
##   Negative 797  99.75%
##      Total 799 100.00%
##  AcVHD_res   n percent valid_percent
##   Positive  10   1.25%         1.25%
##   Negative 787  98.50%        98.75%
##       <NA>   2   0.25%             -
##      Total 799 100.00%       100.00%

TDF Treatment

Create temporary dataset with all ‘TDF’ variables

##  on_TDF_bl   n    percent valid_percent
##      FALSE 175 0.21902378     0.7777778
##       TRUE  50 0.06257822     0.2222222
##         NA 574 0.71839800            NA

Analysis

##  on_TDF_bl   n percent
##         No 749  93.74%
##        Yes  50   6.26%
##      Total 799 100.00%
## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `CI95 = list(...)`.
## Caused by warning:
## ! 'tidy.numeric' is deprecated.
## See help("Deprecated")
## [1] 291.5
## [1] 30.35
## [1] 2455.8

Join ‘TDF_ttt’ with ‘hcv_hdv_markers’ and then with the ‘SchistoHBV’ dataset

LIVER MARKERS

Create temporary dataset with all liver markers

Analysis

##     alt_res   n percent
##  41-80 IU/L  38   4.76%
##    >80 IU/L  14   1.75%
##    ≤40 IU/L 747  93.49%
##       Total 799 100.00%
##    apri_diag   n percent
##     Fibrosis   8   1.00%
##  No Fibrosis 770  96.37%
##      Unknown  21   2.63%
##        Total 799 100.00%
##                fibs_med_diag   n percent
##   F0-1 (no or mild fibrosis) 706  88.36%
##  F2–3 (significant fibrosis)  62   7.76%
##               F4 (cirrhosis)  31   3.88%
##                        Total 799 100.00%
##     fibs7   n percent
##  <7.0 kPa 706  88.36%
##  ≥7.1 kPa  93  11.64%
##     Total 799 100.00%
##     fibs11   n percent
##  <11.0 kPa 768  96.12%
##  ≥11.1 kPa  31   3.88%
##      Total 799 100.00%
##  cap_med_diag   n percent
##        Normal 731  91.49%
##           SLD  68   8.51%
##         Total 799 100.00%

I want to identify how many patients have discordant fibrosis diagnoses between ‘apri_diag’ and ‘fibs_med_diag’

Join ‘liver_markers’ with ‘SchistoHBV’ dataset

URINE MARKERS

Create temporary dataset with all ‘urine markers’ variables needed

Analysis

##                 upcr_diag   n percent
##       Normal (≤0.2 mg/mg)  48   6.01%
##  Proteinuria (>0.2 mg/mg) 751  93.99%
##                     Total 799 100.00%
##     leubu_res   n percent valid_percent
##  Leukocyturia 230  28.79%        28.86%
##        Normal 567  70.96%        71.14%
##          <NA>   2   0.25%             -
##         Total 799 100.00%       100.00%
##  hembu_res   n percent valid_percent
##  Hematuria  97  12.14%        12.17%
##     Normal 700  87.61%        87.83%
##       <NA>   2   0.25%             -
##      Total 799 100.00%       100.00%
##   protbu_res   n percent valid_percent
##       Normal 680  85.11%        85.32%
##  Proteinuria 117  14.64%        14.68%
##         <NA>   2   0.25%             -
##        Total 799 100.00%       100.00%
  • There is a problem with the UPCR ratio right ?
  • NAs ? (n=2)

Join ‘urine_markers’ with ‘SchistoHBV’

ULTRASOUND (US) DATA

Create temporary dataset with all ‘Ultrasound’ variables needed

## # A tibble: 799 × 224
##    pid      hiv_status enrol_d    center med   sen_yn country_orig region_orig
##    <chr>    <chr>      <date>     <fct>  <chr> <fct>  <fct>        <fct>      
##  1 SENB1002 HBV        2019-09-30 SMIT   1     yes    <NA>         Fatick     
##  2 SENB1003 HBV        2019-10-01 SMIT   1     yes    <NA>         Dakar      
##  3 SENB1004 HBV        2019-10-01 SMIT   1     yes    <NA>         Dakar      
##  4 SENB1005 HBV        2019-10-01 SMIT   1     yes    <NA>         Dakar      
##  5 SENB1006 HBV        2019-10-01 SMIT   1     yes    <NA>         Dakar      
##  6 SENB1007 HBV        2019-10-02 SMIT   1     yes    <NA>         Ziguinchor 
##  7 SENB1008 HBV        2019-10-02 SMIT   1     yes    <NA>         Kaolack    
##  8 SENB1009 HBV        2019-10-02 SMIT   1     yes    <NA>         Louga      
##  9 SENB1010 HBV        2019-10-02 SMIT   1     yes    <NA>         Diurbel    
## 10 SENB1011 HBV        2019-10-02 SMIT   1     no     Mali         <NA>       
## # ℹ 789 more rows
## # ℹ 216 more variables: dpt_orig <fct>, res_time <dbl>, reg_act <fct>,
## #   dpt_act <fct>, birth_d <date>, age_en <dbl>, sex <fct>, rank <dbl>,
## #   marital <fct>, union <fct>, child <dbl>, education <fct>, employ <fct>,
## #   other_employ <chr>, health_ins <fct>, hiv_yn <fct>, type_HIV <chr>,
## #   hiv_test1 <fct>, hiv_test1_date <date>, hiv_test2 <fct>,
## #   hiv_test2_date <date>, CD4_last <dbl>, CD4_last_date <date>, …

OTHER MARKERS

Create temporary dataset with all ‘Other markers’ needed

Analysis

##    trad_med   n percent
##        None 529  66.21%
##  Oil/Powder   3   0.38%
##       Plant 267  33.42%
##       Total 799 100.00%
##  eGFR_diag   n percent
##      15-60  73   9.14%
##        ≥60 726  90.86%
##      Total 799 100.00%

Join ‘other_markers’ with ‘SchistoHBV’ dataset

ADD CCA RESULTS

ANALYSIS

Prevalence

## [1] "The prevalence of schistosomiasis among people living with HBV in Dakar is 8.3%"

Table 1

Table 1. Patient Characteristics
Variable Male, N = 4271 Female, N = 3721 Total (N = 799)1 p-value2
Screening Year


0.46
    2019 40 / 427 (9.4%) 42 / 372 (11%) 82 / 799 (10%)
    2020 30 / 427 (7.0%) 29 / 372 (7.8%) 59 / 799 (7.4%)
    2021 226 / 427 (53%) 183 / 372 (49%) 409 / 799 (51%)
    2022 126 / 427 (30%) 117 / 372 (31%) 243 / 799 (30%)
    2023 5 / 427 (1.2%) 1 / 372 (0.3%) 6 / 799 (0.8%)
Age


0.98
    18-31 205 / 427 (48%) 179 / 372 (48%) 384 / 799 (48%)
    >31 222 / 427 (52%) 193 / 372 (52%) 415 / 799 (52%)
Origin


0.10
    <1% prevalence (non-endemic) 130 / 427 (30%) 143 / 371 (39%) 273 / 798 (34%)
    1-9.9% prevalence (low) 105 / 427 (25%) 78 / 371 (21%) 183 / 798 (23%)
    10-49% prevalence (moderate) 137 / 427 (32%) 108 / 371 (29%) 245 / 798 (31%)
    ≥50% prevalence (high) 48 / 427 (11%) 32 / 371 (8.6%) 80 / 798 (10%)
    Other Country 7 / 427 (1.6%) 10 / 371 (2.7%) 17 / 798 (2.1%)
Residence


0.49
    Dakar 347 / 427 (81%) 295 / 372 (79%) 642 / 799 (80%)
    Outside 80 / 427 (19%) 77 / 372 (21%) 157 / 799 (20%)
Alcohol Consumption


0.002
    Yes_past 18 / 404 (4.5%) 3 / 361 (0.8%) 21 / 765 (2.7%)
    No 386 / 404 (96%) 358 / 361 (99%) 744 / 765 (97%)
AUDIT-C Score


0.037
    Misuse 12 / 427 (2.8%) 3 / 372 (0.8%) 15 / 799 (1.9%)
    Negative 415 / 427 (97%) 369 / 372 (99%) 784 / 799 (98%)
Dipstick EtG


0.49
    Positive 73 / 392 (19%) 68 / 329 (21%) 141 / 721 (20%)
    Negative 319 / 392 (81%) 261 / 329 (79%) 580 / 721 (80%)
Drug Consumption


<0.001
    Yes_now 0 / 427 (0%) 0 / 372 (0%) 0 / 799 (0%)
    Yes_past 13 / 427 (3.0%) 0 / 372 (0%) 13 / 799 (1.6%)
    No 414 / 427 (97%) 372 / 372 (100%) 786 / 799 (98%)
Smoking


<0.001
    Yes_now 16 / 427 (3.7%) 2 / 372 (0.5%) 18 / 799 (2.3%)
    Yes_past 69 / 427 (16%) 2 / 372 (0.5%) 71 / 799 (8.9%)
    No 342 / 427 (80%) 368 / 372 (99%) 710 / 799 (89%)
Hypertension 109 / 427 (26%) 83 / 372 (22%) 192 / 799 (24%) 0.29
Diabetes 13 / 427 (3.0%) 9 / 372 (2.4%) 22 / 799 (2.8%) 0.59
BMI


<0.001
    Normal Weight 289 / 426 (68%) 170 / 365 (47%) 459 / 791 (58%)
    Underweight 73 / 426 (17%) 44 / 365 (12%) 117 / 791 (15%)
    Overweight 58 / 426 (14%) 98 / 365 (27%) 156 / 791 (20%)
    Obesity 6 / 426 (1.4%) 53 / 365 (15%) 59 / 791 (7.5%)
Dyslipidemia 160 / 427 (37%) 157 / 372 (42%) 317 / 799 (40%) 0.17
Family History of Liver Disease 8 / 427 (1.9%) 7 / 372 (1.9%) 15 / 799 (1.9%) >0.99
Family History of HCC 45 / 427 (11%) 56 / 372 (15%) 101 / 799 (13%) 0.055
HBV viral load


0.081
    ≤20 IU/mL 69 / 427 (16%) 81 / 372 (22%) 150 / 799 (19%)
    21-2000 IU/mL 235 / 427 (55%) 200 / 372 (54%) 435 / 799 (54%)
    2001-20000 IU/mL 78 / 427 (18%) 66 / 372 (18%) 144 / 799 (18%)
    ≥20000 IU/mL 45 / 427 (11%) 25 / 372 (6.7%) 70 / 799 (8.8%)
qAgHBs


0.81
    <100 IU/mL 38 / 427 (8.9%) 33 / 372 (8.9%) 71 / 799 (8.9%)
    100-1000 IU/mL 57 / 427 (13%) 44 / 372 (12%) 101 / 799 (13%)
    >1000 IU/mL 332 / 427 (78%) 295 / 372 (79%) 627 / 799 (78%)
AgHBe


0.073
    Positive 14 / 427 (3.3%) 5 / 372 (1.3%) 19 / 799 (2.4%)
    Negative 413 / 427 (97%) 367 / 372 (99%) 780 / 799 (98%)
HCV Serology


0.50
    Positive 2 / 427 (0.5%) 0 / 372 (0%) 2 / 799 (0.3%)
    Negative 425 / 427 (100%) 372 / 372 (100%) 797 / 799 (100%)
HDV Serology


0.76
    Positive 6 / 427 (1.4%) 4 / 370 (1.1%) 10 / 797 (1.3%)
    Negative 421 / 427 (99%) 366 / 370 (99%) 787 / 797 (99%)
On TDF at Baseline 31 / 427 (7.3%) 19 / 372 (5.1%) 50 / 799 (6.3%) 0.21
ALAT


0.024
    ≤40 IU/L 390 / 427 (91%) 357 / 372 (96%) 747 / 799 (93%)
    41-80 IU/L 26 / 427 (6.1%) 12 / 372 (3.2%) 38 / 799 (4.8%)
    >80 IU/L 11 / 427 (2.6%) 3 / 372 (0.8%) 14 / 799 (1.8%)
APRI Score


0.015
    Fibrosis 6 / 427 (1.4%) 2 / 372 (0.5%) 8 / 799 (1.0%)
    No Fibrosis 404 / 427 (95%) 366 / 372 (98%) 770 / 799 (96%)
    Unknown 17 / 427 (4.0%) 4 / 372 (1.1%) 21 / 799 (2.6%)
FibroScan LSM


<0.001
    F0-1 (no or mild fibrosis) 347 / 427 (81%) 359 / 372 (97%) 706 / 799 (88%)
    F2–3 (significant fibrosis) 51 / 427 (12%) 11 / 372 (3.0%) 62 / 799 (7.8%)
    F4 (cirrhosis) 29 / 427 (6.8%) 2 / 372 (0.5%) 31 / 799 (3.9%)
FibroScan CAP


0.55
    SLD 34 / 427 (8.0%) 34 / 372 (9.1%) 68 / 799 (8.5%)
    Normal 393 / 427 (92%) 338 / 372 (91%) 731 / 799 (91%)
UPCR Ratio


>0.99
    Proteinuria (>0.2 mg/mg) 393 / 393 (100%) 358 / 358 (100%) 751 / 751 (100%)
    Normal (<0.2 mg/mg) 0 / 393 (0%) 0 / 358 (0%) 0 / 751 (0%)
Leukocyturia (Urine Strips)


<0.001
    Leukocyturia 57 / 426 (13%) 173 / 371 (47%) 230 / 797 (29%)
    Normal 369 / 426 (87%) 198 / 371 (53%) 567 / 797 (71%)
Hematuria (Urine Strips)


<0.001
    Hematuria 29 / 426 (6.8%) 68 / 371 (18%) 97 / 797 (12%)
    Normal 397 / 426 (93%) 303 / 371 (82%) 700 / 797 (88%)
Proteinuria (Urine Strips)


0.62
    Proteinuria 65 / 426 (15%) 52 / 371 (14%) 117 / 797 (15%)
    Normal 361 / 426 (85%) 319 / 371 (86%) 680 / 797 (85%)
Traditional Medicine


0.61
    Plant 148 / 427 (35%) 119 / 372 (32%) 267 / 799 (33%)
    Oil/Powder 2 / 427 (0.5%) 1 / 372 (0.3%) 3 / 799 (0.4%)
    None 277 / 427 (65%) 252 / 372 (68%) 529 / 799 (66%)
eGFR


<0.001
    ≥60 406 / 427 (95%) 320 / 372 (86%) 726 / 799 (91%)
    15-60 21 / 427 (4.9%) 52 / 372 (14%) 73 / 799 (9.1%)
CCA Result


0.52
    Positive 30 / 389 (7.7%) 30 / 332 (9.0%) 60 / 721 (8.3%)
    Negative 359 / 389 (92%) 302 / 332 (91%) 661 / 721 (92%)
1 n / N (%)
2 Fisher’s exact test; Pearson’s Chi-squared test

Table 2

## 78 observations missing `cca_res` have been removed. To include these observations, use `forcats::fct_na_value_to_level()` on `cca_res` column before passing to `tbl_summary()`.
Table 2. Patient Characteristics
Variable CCA Result Total (N = 721)1 p-value2
Positive, N = 601 Negative, N = 6611
Screening Year


<0.001
    2019 20 / 60 (33%) 61 / 661 (9.2%) 81 / 721 (11%)
    2020 2 / 60 (3.3%) 57 / 661 (8.6%) 59 / 721 (8.2%)
    2021 36 / 60 (60%) 343 / 661 (52%) 379 / 721 (53%)
    2022 2 / 60 (3.3%) 195 / 661 (30%) 197 / 721 (27%)
    2023 0 / 60 (0%) 5 / 661 (0.8%) 5 / 721 (0.7%)
Age


0.42
    18-31 32 / 60 (53%) 317 / 661 (48%) 349 / 721 (48%)
    >31 28 / 60 (47%) 344 / 661 (52%) 372 / 721 (52%)
Sex


0.52
    Male 30 / 60 (50%) 359 / 661 (54%) 389 / 721 (54%)
    Female 30 / 60 (50%) 302 / 661 (46%) 332 / 721 (46%)
Origin


0.69
    <1% prevalence (non-endemic) 17 / 59 (29%) 230 / 661 (35%) 247 / 720 (34%)
    1-9.9% prevalence (low) 12 / 59 (20%) 150 / 661 (23%) 162 / 720 (23%)
    10-49% prevalence (moderate) 21 / 59 (36%) 204 / 661 (31%) 225 / 720 (31%)
    ≥50% prevalence (high) 8 / 59 (14%) 62 / 661 (9.4%) 70 / 720 (9.7%)
    Other Country 1 / 59 (1.7%) 15 / 661 (2.3%) 16 / 720 (2.2%)
Residence


0.22
    Dakar 52 / 60 (87%) 530 / 661 (80%) 582 / 721 (81%)
    Outside 8 / 60 (13%) 131 / 661 (20%) 139 / 721 (19%)
Alcohol Consumption


>0.99
    Yes_past 1 / 59 (1.7%) 18 / 631 (2.9%) 19 / 690 (2.8%)
    No 58 / 59 (98%) 613 / 631 (97%) 671 / 690 (97%)
AUDIT-C Score


>0.99
    Misuse 1 / 60 (1.7%) 12 / 661 (1.8%) 13 / 721 (1.8%)
    Negative 59 / 60 (98%) 649 / 661 (98%) 708 / 721 (98%)
Dipstick EtG


0.023
    Positive 5 / 57 (8.8%) 135 / 629 (21%) 140 / 686 (20%)
    Negative 52 / 57 (91%) 494 / 629 (79%) 546 / 686 (80%)
Drug Consumption


>0.99
    Yes_now 0 / 60 (0%) 0 / 661 (0%) 0 / 721 (0%)
    Yes_past 1 / 60 (1.7%) 10 / 661 (1.5%) 11 / 721 (1.5%)
    No 59 / 60 (98%) 651 / 661 (98%) 710 / 721 (98%)
Smoking


0.45
    Yes_now 0 / 60 (0%) 15 / 661 (2.3%) 15 / 721 (2.1%)
    Yes_past 7 / 60 (12%) 57 / 661 (8.6%) 64 / 721 (8.9%)
    No 53 / 60 (88%) 589 / 661 (89%) 642 / 721 (89%)
Hypertension 11 / 60 (18%) 164 / 661 (25%) 175 / 721 (24%) 0.26
Diabetes 2 / 60 (3.3%) 18 / 661 (2.7%) 20 / 721 (2.8%) 0.68
BMI


0.87
    Normal Weight 35 / 60 (58%) 378 / 655 (58%) 413 / 715 (58%)
    Underweight 10 / 60 (17%) 99 / 655 (15%) 109 / 715 (15%)
    Overweight 12 / 60 (20%) 124 / 655 (19%) 136 / 715 (19%)
    Obesity 3 / 60 (5.0%) 54 / 655 (8.2%) 57 / 715 (8.0%)
Dyslipidemia 20 / 60 (33%) 274 / 661 (41%) 294 / 721 (41%) 0.22
Family History of Liver Disease 0 / 60 (0%) 12 / 661 (1.8%) 12 / 721 (1.7%) 0.61
Family History of HCC 5 / 60 (8.3%) 86 / 661 (13%) 91 / 721 (13%) 0.30
HBV viral load


0.98
    ≤20 IU/mL 10 / 60 (17%) 123 / 661 (19%) 133 / 721 (18%)
    21-2000 IU/mL 33 / 60 (55%) 356 / 661 (54%) 389 / 721 (54%)
    2001-20000 IU/mL 11 / 60 (18%) 123 / 661 (19%) 134 / 721 (19%)
    ≥20000 IU/mL 6 / 60 (10%) 59 / 661 (8.9%) 65 / 721 (9.0%)
qAgHBs


0.35
    <100 IU/mL 8 / 60 (13%) 54 / 661 (8.2%) 62 / 721 (8.6%)
    100-1000 IU/mL 6 / 60 (10%) 85 / 661 (13%) 91 / 721 (13%)
    >1000 IU/mL 46 / 60 (77%) 522 / 661 (79%) 568 / 721 (79%)
AgHBe


0.65
    Positive 2 / 60 (3.3%) 15 / 661 (2.3%) 17 / 721 (2.4%)
    Negative 58 / 60 (97%) 646 / 661 (98%) 704 / 721 (98%)
HCV Serology


>0.99
    Positive 0 / 60 (0%) 1 / 661 (0.2%) 1 / 721 (0.1%)
    Negative 60 / 60 (100%) 660 / 661 (100%) 720 / 721 (100%)
HDV Serology


0.50
    Positive 1 / 59 (1.7%) 7 / 660 (1.1%) 8 / 719 (1.1%)
    Negative 58 / 59 (98%) 653 / 660 (99%) 711 / 719 (99%)
On TDF at Baseline 2 / 60 (3.3%) 41 / 661 (6.2%) 43 / 721 (6.0%) 0.57
ALAT


>0.99
    ≤40 IU/L 56 / 60 (93%) 616 / 661 (93%) 672 / 721 (93%)
    41-80 IU/L 3 / 60 (5.0%) 33 / 661 (5.0%) 36 / 721 (5.0%)
    >80 IU/L 1 / 60 (1.7%) 12 / 661 (1.8%) 13 / 721 (1.8%)
APRI Score


0.30
    Fibrosis 0 / 60 (0%) 8 / 661 (1.2%) 8 / 721 (1.1%)
    No Fibrosis 57 / 60 (95%) 638 / 661 (97%) 695 / 721 (96%)
    Unknown 3 / 60 (5.0%) 15 / 661 (2.3%) 18 / 721 (2.5%)
FibroScan LSM


0.14
    F0-1 (no or mild fibrosis) 56 / 60 (93%) 580 / 661 (88%) 636 / 721 (88%)
    F2–3 (significant fibrosis) 1 / 60 (1.7%) 55 / 661 (8.3%) 56 / 721 (7.8%)
    F4 (cirrhosis) 3 / 60 (5.0%) 26 / 661 (3.9%) 29 / 721 (4.0%)
FibroScan CAP


0.14
    SLD 2 / 60 (3.3%) 59 / 661 (8.9%) 61 / 721 (8.5%)
    Normal 58 / 60 (97%) 602 / 661 (91%) 660 / 721 (92%)
UPCR Ratio


>0.99
    Proteinuria (>0.2 mg/mg) 60 / 60 (100%) 618 / 618 (100%) 678 / 678 (100%)
    Normal (<0.2 mg/mg) 0 / 60 (0%) 0 / 618 (0%) 0 / 678 (0%)
Leukocyturia (Urine Strips)


0.88
    Leukocyturia 17 / 60 (28%) 193 / 659 (29%) 210 / 719 (29%)
    Normal 43 / 60 (72%) 466 / 659 (71%) 509 / 719 (71%)
Hematuria (Urine Strips)


0.67
    Hematuria 6 / 60 (10%) 78 / 659 (12%) 84 / 719 (12%)
    Normal 54 / 60 (90%) 581 / 659 (88%) 635 / 719 (88%)
Proteinuria (Urine Strips)


0.83
    Proteinuria 10 / 60 (17%) 103 / 659 (16%) 113 / 719 (16%)
    Normal 50 / 60 (83%) 556 / 659 (84%) 606 / 719 (84%)
Traditional Medicine


0.72
    Plant 18 / 60 (30%) 222 / 661 (34%) 240 / 721 (33%)
    Oil/Powder 0 / 60 (0%) 2 / 661 (0.3%) 2 / 721 (0.3%)
    None 42 / 60 (70%) 437 / 661 (66%) 479 / 721 (66%)
eGFR


0.32
    ≥60 52 / 60 (87%) 599 / 661 (91%) 651 / 721 (90%)
    15-60 8 / 60 (13%) 62 / 661 (9.4%) 70 / 721 (9.7%)
1 n / N (%)
2 Fisher’s exact test; Pearson’s Chi-squared test

Plots

Prevalence by Origin

Prevalence by HBV viral load

Prevalence by qAgHBs

Prevalence by LSM (categorized)

Prevalence by LSM ≥7.1 kPa

Prevalence by LSM ≥11.1 kPa

Prevalence by eGFR